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Dehankar, M. K.

Publications and source records attributed to Dehankar, M. K..

2 recordsLinked to original sources

A comprehensive view of somatic mosaicism by single-cell DNA analysis

Single-cell DNA sequencing offers a powerful means of studying somatic mosaicism but requires careful analysis to mitigate DNA amplification-related artifacts. We performed primary template-directed amplification (PTA) and sequencing of 102 nuclei from postmortem lung and colon tissues of a 74-year-old male. Single-cell mutation burdens and spectra were validated by duplex sequencing and revealed heterogeneity across organs and cells, including signatures of APOBEC activity and tobacco exposure. Cells from both tissues exhibited chromosomal aneuploidies, loss of chromosome Y, and chromosomal rearrangements including rearrangements of the T-cell receptor loci indicative of T-cells. Shared embryonic mutations between cells enabled reconstruction of cellular ancestries from the zygote, which were validated by bulk sequencing. Collectively, we demonstrate a comprehensive approach for single-cell genomics that yields an expansive view of diverse somatic mutation types from development through aging across diverse tissues--insights that are obscured in bulk sequencing and only partially captured by other single-cell methods.

genomics↗

Single cell whole genome and transcriptome sequencing links somatic mutations to cell identity and ancestry

The role of somatic mutations in human development and disease is obscured by difficulties in characterizing mutations at the single cell level and identifying cell types carrying them. Here we analysed somatic genomes of clonal iPSC lines and of single-cells after whole-genome amplification (scWGA) by PTA and ResolveOme from skin fibroblasts, blood and urine of a live donor. Mutation burden and spectra converged across approaches, revealing heterogeneous mutational footprints across cells driven by environmental exposures (UV damage and chemotherapy) and lymphocyte differentiation. Aneuploidies in single cells were detected by all the approaches and were orthogonally validated by Strand-seq. Uniquely, ResolveOme enabled cell-type identification using single-cell transcriptomes. Using a newly developed method accounting for noise and allele drop-out in scWGA, we de novo reconstructed the cell phylogenetic tree for this donor. Together, scWGA establishes a powerful foundation for comprehensive, cell type-aware, lineage-aware profiling of somatic mutations at single cell level.

genomics↗